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OBJECTIVE: To test whether adding mobile application coaching and patient/provider web portals to community primary care compared with standard diabetes management would reduce glycated hemoglobin levels in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: A cluster-randomized clinical trial, the Mobile Diabetes Intervention Study, randomly assigned 26 primary care practices to one of three stepped treatment groups or a control group (usual care). A total of 163 patients were enrolled and included in analysis. The primary outcome was change in glycated hemoglobin levels over a 1-year treatment period. Secondary outcomes were changes in patient-reported diabetes symptoms, diabetes distress, depression, and other clinical (blood pressure) and laboratory (lipid) values. Maximal treatment was a mobile- and web-based self-management patient coaching system and provider decision support. Patients received automated, real-time educational and behavioral messaging in response to individually analyzed blood glucose values, diabetes medications, and lifestyle behaviors communicated by mobile phone. Providers received quarterly reports summarizing patient’s glycemic control, diabetes medication management, lifestyle behaviors, and evidence-based treatment options. RESULTS: The mean declines in glycated hemoglobin were 1.9% in the maximal treatment group and 0.7% in the usual care group, a difference of 1.2% (P < 0.001) over 12 months. Appreciable differences were not observed between groups for patient-reported diabetes distress, depression, diabetes symptoms, or blood pressure and lipid levels (all P > 0.05). CONCLUSIONS: The combination of behavioral mobile coaching with blood glucose data, lifestyle behaviors, and patient self-management data individually analyzed and presented with evidence-based guidelines to providers substantially reduced glycated hemoglobin levels over 1 year.

Background. We sought to describe the temporal relationship between vaginal microbiota and human papillomavirus (HPV) detection. Methods. Thirty-two reproductive-age women self-collected midvaginal swabs twice weekly for 16 weeks (937 samples). Vaginal bacterial communities were characterized by pyrosequencing of barcoded 16S rRNA genes and clustered into 6 community state types (CSTs). Each swab was tested for 37 HPV types. The effects of CSTs on the rate of transition between HPV-negative and HPV-positive states were assessed using continuous-time Markov models. Results. Participants had an average of 29 samples, with HPV point prevalence between 58%-77%. CST was associated with changes in HPV status (P<.001). Lactobacillus gassen-dominated CSTs had the fastest HPV remission rate, and a low Lactobacillus community with high proportions of the genera Atopobium (CST IV-B) had the slowest rate compared to L. crispat ws-dominated CSTs (adjusted transition rate ratio [aTRR], 4.43, 95% confidence interval [CI], 1.11-17.7; aTRR, 0.33, 95% CI, .12-1.19, respectively). The rate ratio of incident HPV for low Lactobacillus CST IV-Awas 1.86 (95% CI, .52-6.74). Conclusions. Vaginal microbiota dominated by L. gasseri was associated with increased clearance of detectable HPV. Frequent longitudinal sampling is necessary for evaluation of the association between HPV detection and dynamic microbiota.

Background The high prevalence of methicillin-resistant S. aureus (MRSA) has led clinicians to select antibiotics that have coverage against MRSA, usually vancomycin, for empiric therapy for suspected staphylococcal infections. Clinicians often continue vancomycin started empirically even when methicillin-susceptible S. aureus (MSSA) strains are identified by culture. However, vancomycin has been associated with poor outcomes such as nephrotoxicity, persistent bacteremia and treatment failure. The objective of this study was to compare the effectiveness of vancomycin versus the beta-lactam antibiotics nafcillin and cefazolin among patients with MSSA bacteremia. The outcome of interest for this study was 30-day in-hospital mortality. Methods This retrospective cohort study included all adult in-patients admitted to a tertiary-care facility between January 1, 2003 and June 30, 2007 who had a positive blood culture for MSSA and received nafcillin, cefazolin or vancomycin. Cox proportional hazard models were used to assess independent mortality hazards comparing nafcillin or cefazolin versus vancomycin. Similar methods were used to estimate the survival benefits of switching from vancomycin to nafcillin or cefazolin versus leaving patients on vancomycin. Each model included statistical adjustment using propensity scores which contained variables associated with an increased propensity to receive vancomycin. Results 267 patients were included; 14% (38/267) received nafcillin or cefazolin, 51% (135/267) received both vancomycin and either nafcillin or cefazolin, and 35% (94/267) received vancomycin. Thirty (11%) died within 30 days. Those receiving nafcillin or cefazolin had 79% lower mortality hazards compared with those who received vancomycin alone (adjusted hazard ratio (HR): 0.21; 95% confidence interval (CI): 0.09, 0.47). Among the 122 patients who initially received vancomycin empirically, those who were switched to nafcillin or cefazolin (66/122) had 69% lower mortality hazards (adjusted HR: 0.31; 95% CI: 0.10, 0.95) compared to those who remained on vancomycin. Conclusions Receipt of nafcillin or cefazolin was protective against mortality compared to vancomycin even when therapy was altered after culture results identified MSSA. Convenience of vancomycin dosing may not outweigh the potential benefits of nafcillin or cefazolin in the treatment of MSSA bacteremia.

Background. Liver-related mortality among those infected with hepatitis C virus (HCV) has been described, but little is known about non—liver-related mortality. Our objective was to determine HCV-associated all-cause, liver-, and non—liver-related mortality in the general US population. Methods. A prospective cohort study of 9378 nationally representative adults aged 17—59 years was performed utilizing the Third National Health and Nutrition Examination Survey (NHANES III) Linked Mortality File that was made publicly available in 2010. HCV status was assessed from 1988 to 1994, with mortality follow-up of the same individuals through 2006. Results. There were 614 deaths over a median follow-up of 14.8 years. After adjusting for all covariate risk factors, HCV chronic infection had a 2.37 times higher all-cause mortality rate ratio [MRR] (95% CI: 1.28—4.38; P = .008), a 26.46 times higher liver-related MRR (95% CI: 8.00—87.48; P < .001), and 1.79 times higher non—liver-related MRR (95% CI:.77—4.19; P = .18), compared with being HCV-negative. This represents an estimated 2.46 million US adults aged 17—59 years with chronic HCV infection who had an estimated 31,163 deaths from all causes per year, of which 57.8% (95% CI: 21.9%—77.2%) were attributable to HCV. Among those, there was an estimated 9569 liver-related deaths per year, of which 96.2% (95% CI: 87.5—98.9%) were attributable to HCV. Non—liver-related deaths were not significantly associated with HCV status. Conclusions. Chronic HCV all-cause mortality is more than twice that of HCV-negative individuals. This suggests that those with chronic HCV infection are at a higher risk of death even after accounting for liver-related morbidity and should be closely monitored.

A decline of skeletal muscle strength with aging is a primary cause of mobility loss and frailty in older persons, but the molecular mechanisms of such decline are not understood. Here, we performed quantitative proteomic analysis from skeletal muscle collected from 58 healthy persons aged 20 to 87 years. In muscle from older persons, ribosomal proteins and proteins related to energetic metabolism, including those related to the TCA cycle, mitochondria respiration, and glycolysis, were underrepresented, while proteins implicated in innate and adaptive immunity, proteostasis, and alternative splicing were overrepresented. Consistent with reports in animal models, older human muscle was characterized by deranged energetic metabolism, a pro-inflammatory environment and increased proteolysis. Changes in alternative splicing with aging were confirmed by RNA-seq analysis. We propose that changes in the splicing machinery enables muscle cells to respond to a rise in damage with aging. As humans age, their muscles become weaker, making it increasingly harder for them to move, a condition known as sarcopenia. Analyzing old muscles in other animals revealed that they produce energy inefficiently, they destroy more proteins than younger muscles, and they have high levels of molecules that cause inflammation. These characteristics may be involved in causing muscle weakness. Proteomics is the study of proteins, the molecules that play many roles in keeping the body working: for example, they accelerate chemical reactions, participate in copying DNA and help cells respond to stimuli. Using proteomics, it is possible to examine a large number of the different proteins in a tissue, which can provide information about the state of that tissue. Ubaida-Mohien et al. used this approach to answer the question of why muscles become weaker with age. First, they analyzed the levels of all the proteins found in skeletal muscle collected from 58 healthy volunteers between 20 and 87 years of age. This revealed that the muscles of older people have fewer copies of the proteins that make up ribosomes – the cellular machines that produce new proteins – and fewer proteins involved in providing the cell with chemical energy. In contrast, proteins implicated in the immune system, in the maintenance of existing proteins, and in processing other molecules called RNAs were more abundant in older muscles. Ubaida-Mohien et al. then looked more closely at changes involving RNA processing. Cells make proteins by copying DNA sequences into an RNA template and using this template to instruct the ribosomes on how to make the specific protein. Before the RNA can be ‘read’ by a ribosome, however, some parts must be cut out and others added, which can lead to different versions of the final RNA, also known as alternative transcripts. In order to check whether the difference in the levels of proteins that process RNAs was affecting the RNAs being produced, Ubaida-Mohien et al. extracted the RNAs from older and younger muscles and compared them. This showed that the RNA in older people had more alternative transcripts, confirming that the change in protein levels was having downstream effects. Currently, it is not possible to prevent or delay the loss of muscle strength associated with aging. Understanding how the protein make-up of muscles changes as humans grow older may help find new ways to prevent and perhaps even reverse this decline.

Background Most US individuals with ADRD are women. Menopause typically occurs in midlife, accompanied by declines in potentially neuroprotective and cardioprotective hormones. Earlier menopause leads to earlier exposure to decreased sex hormones, potentially increasing ADRD risk. Surgical menopause via reproductive surgeries may cause a sudden loss of hormones and may represent be a modifiable risk factor for ADRD. We aimed to determine if menopausal type and age are associated with physician adjudicated‐ADRD diagnoses. Methods Data were from 3371 women prospectively followed from age 65+ years. Participants self‐reported age of menopause and/or surgeries, hormone‐therapy, education, and race (Black/White). For this study, given the bleeding criteria for determining final menstrual period, both hysterectomy and/or bilateral oophorectomy were considered surgical menopause. Adjudication for ADRD was conducted in two sub‐samples (n = 2397) by neurologist/psychiatrist committee review using neuropsychological tests, neurological examinations, medical records, physician questionnaires, and proxy/informant interviews. All participants had auxiliary information including medication, ICD‐9 codes, proxy reports, and death certificates. For women not included in the physician‐adjudicated sub‐sample, multiple imputation was used to impute adjudicated ADRD status and age of diagnoses (bounded regression) using auxiliary information. Cox proportional hazard models were used for estimations controlling for 1) hormone use and 2) additionally demographics and health behaviors. Result The mean menopausal age was 46.6 years (SE=0.12); 30.5% of participants underwent surgical menopause (19.0% via hysterectomy and 10.9% via bilateral oophorectomy), and 45% used hormone therapy. After imputation, 63.6% were diagnosed with ADRD (Table 1). Older menopausal age was associated with a lower hazard of ADRD controlling for hormone use (HR=0.94[0.90,0.98]) and fully adjusted (HR=0.97[0.93,1.01]). Surgical menopause was linked with 1.22 [1.12,1.33] higher hazard of ADRD controlling for hormones, and 1.15 [1.04,1.25] higher hazard with full adjustment. In an additional model with age and type of menopause included together, menopausal age was non‐significant and surgical menopause remained positively associated with ADRD (HR=1.12 [1.00,1.25]). Conclusion Although further work is needed to obtain more precise measures of menopause, results suggest older menopausal age may modestly reduce ADRD risk non‐independent of type, and that reproductive surgeries are associated with a higher risk of ADRD.

Purpose This study evaluates sex differences and predictors of anti-osteoporosis medication (AOM) use following a hip fracture, with a focus on older men who exhibit higher mortality rates post-fracture compared to women over the age of 65. Methods Participants included 151 men and 161 women aged 65 and older with hip fractures. The outcome, AOM use, was assessed at baseline (≤ 22 days of hospitalization) and at 2, 6, and 12 months post-hip fracture. Generalized estimating equations (GEE) modeled sex differences and predictors of AOM use during the year post-fracture in 255 participants with complete baseline data and ≥ 1 follow-up observation. Results Of the 312 participants, only 53 used AOM at baseline, and 35 initiated use during follow-up. In the unadjusted GEE model, AOM use was significantly less likely in men (OR = 0.42; 95% CI, 0.22–0.78) compared to women. For both men and women, baseline use of AOM was a significant predictor (OR = 28.3; 95% CI, 5.4–148.0 vs. 41.6; 95% CI, 14.0–123.0). The other significant predictors by sex were osteoporosis diagnosis (OR = 3.19; 95% CI, 1.16–8.77) and minimal alcohol use (OR = 3.26; 95% CI, 1.34–7.94) for women versus age (OR = 1.09; 95% CI, 1.01–1.18) for men. Conclusion In older adults with hip fractures, AOM use is low over the year post-fracture and men are less likely to report AOM use compared to women which has implications for important sex differences in predictors of use. Further research is needed to address overall disparities and sex differences in AOM use.

Background Alzheimer's disease (AD) cognitive decline is correlated with tau neuropathology, which can follow a typical spatiotemporal pattern spreading from the medial temporal lobe to the lateral temporal, parietal, frontal, and occipital lobes, at varying levels of severity. However, atypical patterns have also been observed in postmortem studies. Recently, more studies have used hypothesis‐driven partitioning informed by postmortem findings or data‐driven clustering with in‐vivo imaging data to identify AD subtypes with distinct clinical characteristics. However, major challenges remain: (i) AD subtypes are poorly defined, and are difficult to compare, especially at the individual‐level, (ii) Distinct pathological tau and gray matter atrophy patterns characterize AD subtypes, but the unique spatiotemporal relationship is unknown. Thus, the present study proposes a framework to aid interpretation of subtype replicability by utilizing hypothesis‐driven postmortem data to guide data‐driven clustering. Method Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used. The likelihood of an AD subtype was computed for each participant by counting their subtype assignment across common hypothesis‐driven protocols. Semi‐supervised clustering made partial use of this likelihood to cluster the ADNI data, and assignment consistency and stability were compared to unsupervised clustering using a nested cross‐validation design. Clustering from samples with cross‐sectional tau features (n =  222), and separately, atrophy‐related features (n =  527) were then evaluated. Result When evaluated on a held‐out test set of participants with a high likelihood for different AD subtypes, semi‐supervised clustering trained on a portion of “high‐likelihood participants” demonstrated consistent subtype prediction on the held‐out test set (median adjusted rand index of 0.57 when using tau features, and 0.12 when using atrophy‐related features), whereas typical unsupervised methods (e.g., hierarchical, k‐means) produced chance‐level clustering. Conclusion The results demonstrate the risk of relying on unsupervised clustering for interpreting AD subtypes and recommend a framework for defining the subtypes, training clustering, and evaluating performance based on hypothesis‐driven definitions grounded in the original postmortem findings.

Alzheimer's disease (AD) cognitive decline is correlated with tau neuropathology, which can follow a typical spatiotemporal pattern spreading from the medial temporal lobe to the lateral temporal, parietal, frontal, and occipital lobes, at varying levels of severity. However, atypical patterns have also been observed in postmortem studies. Recently, more studies have used hypothesis-driven partitioning informed by postmortem findings or data-driven clustering with in-vivo imaging data to identify AD subtypes with distinct clinical characteristics. However, major challenges remain: (i) AD subtypes are poorly defined, and are difficult to compare, especially at the individual-level, (ii) Distinct pathological tau and gray matter atrophy patterns characterize AD subtypes, but the unique spatiotemporal relationship is unknown. Thus, the present study proposes a framework to aid interpretation of subtype replicability by utilizing hypothesis-driven postmortem data to guide data-driven clustering. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used. The likelihood of an AD subtype was computed for each participant by counting their subtype assignment across common hypothesis-driven protocols. Semi-supervised clustering made partial use of this likelihood to cluster the ADNI data, and assignment consistency and stability were compared to unsupervised clustering using a nested cross-validation design. Clustering from samples with cross-sectional tau features (n =  222), and separately, atrophy-related features (n =  527) were then evaluated. When evaluated on a held-out test set of participants with a high likelihood for different AD subtypes, semi-supervised clustering trained on a portion of \"high-likelihood participants\" demonstrated consistent subtype prediction on the held-out test set (median adjusted rand index of 0.57 when using tau features, and 0.12 when using atrophy-related features), whereas typical unsupervised methods (e.g., hierarchical, k-means) produced chance-level clustering. The results demonstrate the risk of relying on unsupervised clustering for interpreting AD subtypes and recommend a framework for defining the subtypes, training clustering, and evaluating performance based on hypothesis-driven definitions grounded in the original postmortem findings.

(R,S)-Ketamine produces rapid, robust, and sustained antidepressant effects in major depressive disorder. Specifically, its pharmacological efficacy in treatment refractory depression is considered a major breakthrough in the field. However, the mechanism of action of ketamine’s rapid effect remains to be determined. In order to identify pathways that are responsible for ketamine’s effect, a targeted metabolomic approach was carried out using a double-blind, placebo-controlled crossover design, with infusion order randomized with medication-free patients with treatment-resistant major depressive disorder (29 subjects) and healthy controls (25 subjects). The metabolomic profile of these subjects was characterized at multiple time points, and a comprehensive analysis was investigated between the following: MDD and healthy controls, treatment and placebo in both groups and the corresponding response to ketamine treatment. Ketamine treatment resulted in a general increase in circulating sphingomyelins, levels which were not correlated with response. Ketamine response resulted in more pronounced effects in the kynurenine pathway and the arginine pathway at 4 h post-infusion, where a larger decrease in circulating kynurenine levels and a larger increase in the bioavailability of arginine were observed in responders to ketamine treatment, suggesting possible mechanisms for response to ketamine treatment.